Evidence map›Paper›PMID 38875560›Full record

ArticleJMIR AI2023

Association of Health Care Work With Anxiety and Depression During the COVID-19 Pandemic: Structural Topic Modeling Study.

Matteo Malgaroli, Emily Tseng, Thomas D Hull, Emma Jennings, Tanzeem K Choudhury, Naomi M Simon

Abstract read
In one paragraph

Article in JMIR AI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Matteo MalgaroliDepartment of Psychiatry, Grossman School of Medicine, New York University, New York, NY, United States.ORCID https://orcid.org/0000-0003-4209-6939
Emily TsengAnn S Bowers College of Computing and Information Science, Cornell University, Ithaca, NY, United States.ORCID https://orcid.org/0000-0003-1087-1101
Thomas D HullResearch and Development, Talkspace, New York, NY, United States.ORCID https://orcid.org/0000-0001-8275-6765
Emma JenningsDepartment of Psychiatry, Grossman School of Medicine, New York University, New York, NY, United States.ORCID https://orcid.org/0009-0000-5339-5200
Tanzeem K ChoudhuryAnn S Bowers College of Computing and Information Science, Cornell University, Ithaca, NY, United States.ORCID https://orcid.org/0000-0002-5952-4955
Naomi M SimonDepartment of Psychiatry, Grossman School of Medicine, New York University, New York, NY, United States.ORCID https://orcid.org/0000-0002-8867-889X

Funding

Deep Learning Based Natural Language Processing Markers of Anxiety and DepressionK23MH134068 · NIMH · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Matteo Malgaroli · 2023 to 2026
$982k
NIMH NIH HHS K23 MH134068
6 · The paper itself

Abstract

backgroundStressors for health care workers (HCWs) during the COVID-19 pandemic have been manifold, with high levels of depression and anxiety alongside gaps in care. Identifying the factors most tied to HCWs' psychological challenges is crucial to addressing HCWs' mental health needs effectively, now and for future large-scale events.

objectiveIn this study, we used natural language processing methods to examine deidentified psychotherapy transcripts from telemedicine treatment during the initial wave of COVID-19 in the United States. Psychotherapy was delivered by licensed therapists while HCWs were managing increased clinical demands and elevated hospitalization rates, in addition to population-level social distancing measures and infection risks. Our goal was to identify specific concerns emerging in treatment for HCWs and to compare differences with matched non-HCW patients from the general population.

methodsWe conducted a case-control study with a sample of 820 HCWs and 820 non-HCW matched controls who received digitally delivered psychotherapy in 49 US states in the spring of 2020 during the first US wave of the COVID-19 pandemic. Depression was measured during the initial assessment using the Patient Health Questionnaire-9, and anxiety was measured using the General Anxiety Disorder-7 questionnaire. Structural topic models (STMs) were used to determine treatment topics from deidentified transcripts from the first 3 weeks of treatment. STM effect estimators were also used to examine topic prevalence in patients with moderate to severe anxiety and depression.

resultsThe median treatment enrollment date was April 15, 2020 (IQR March 31 to April 27, 2020) for HCWs and April 19, 2020 (IQR April 5 to April 27, 2020) for matched controls. STM analysis of deidentified transcripts identified 4 treatment topics centered on health care and 5 on mental health for HCWs. For controls, 3 STM topics on pandemic-related disruptions and 5 on mental health were identified. Several STM treatment topics were significantly associated with moderate to severe anxiety and depression, including working on the hospital unit (topic prevalence 0.035, 95% CI 0.022-0.048; P<.001), mood disturbances (prevalence 0.014, 95% CI 0.002-0.026; P=.03), and sleep disturbances (prevalence 0.016, 95% CI 0.002-0.030; P=.02). No significant associations emerged between pandemic-related topics and moderate to severe anxiety and depression for non-HCW controls.

conclusionsThe study provides large-scale quantitative evidence that during the initial wave of the COVID-19 pandemic, HCWs faced unique work-related challenges and stressors associated with anxiety and depression, which required dedicated treatment efforts. The study further demonstrates how natural language processing methods have the potential to surface clinically relevant markers of distress while preserving patient privacy.

Indexed as

anxietyCOVID-19depressiondigital healthhealth care workersmental healthnatural language processingpsychotherapystressortopic modelingtreatment

Identifiers

PMID38875560
PMCPMC11041488

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.